I have recently read with interest the article by Barac and colleagues.[1] In their article, the authors described the advantage of serum fibrinogen, not C-reactive protein (CRP), as a significant contributor of incident heart failure (HF). Diabetes mellitus contributed significantly to the incident HF, and concomitant presence of inflammatory markers showed a different effect on the incident HF stratified by the existence of albuminuria. In contrast, the metabolic syndrome did not contribute to the incident HF.

Recently, members of the Emerging Risk Factors Collaboration[2] reported a meta-analysis for the prediction of cardiovascular disease (CVD) by the same inflammatory markers, CRP and fibrinogen. They concluded the advantage of additional measures of serum CRP or fibrinogen for the prediction of a CVD event. However, the contribution degree of CRP was stronger than that of fibrinogen. Their meta-analysis was mainly evaluated by the C-index, whose statistical indicator was based on receiver operating characteristic curve analysis.[3]

The cumulative hazards and 95% confidence intervals for HF should be calculated in each category by using two inflammatory markers, presented in Barac and colleagues' Figure. In the Figure, they presented the superiority of serum fibrinogen, compared with CRP, as a predictor of HF. They also described that the elevated CRP did not significantly increase incident HF risk, but elevated fibrinogen was associated with a significant increase in HF risk in their Table II. They used Cox multivariate regression analysis by adjusting several conventional risk factors for HF. But, unfortunately, CRP and fibrinogen could not be handled simultaneously as successfully conducted in their Figure. The hazard ratio and 95% confidence interval of each independent variable was calculated by adjusting other independent variables being their mean values. If they classify individuals with both CRP and fibrinogen levels <75th percentile as a control group (referent), hazard ratios of the other 3 groups on inflammatory markers would be clarified in their Table II. The same pitfall was also observed in a paper by Urbonaviciene and colleagues.[4] By converting serum CRP and plasma alpha-defensin binary, 4 categorical data on inflammatory markers would be prepared as the independent variable for Cox multivariate regression analysis.

Converting each inflammation marker into binary data and making categorical combination is useful for the risk assessment for the prediction of incident HF or CVD. The Kaplan-Meier plot of cumulative hazards for incident HF is a univariate statistical procedure, and categorical handling of the target independent variables for Cox multivariate regression analysis is required to know the contribution of each risk factor for incident HF.